Project summary/Abstract Positive surgical margin (PSM) has been shown to lead to a moderate increase of local relapse in nephrectomy, especially nephron-sparing surgery for small renal masses due to the increased likelihood of leaving residual cancer in the remaining kidney. Achieving negative surgical margins during partial nephrectomy would help decrease reoperation rate, relieve patient anxiety and costs and potentially improve patient outcome. The most common technique for minimizing the risk of PSM, namely intraoperative frozen section (IFS), is time-consuming and expensive, requires a trained pathologist, and is particularly susceptible to artifacts with kidney tissue.!A better technique to accurately detect PSM with fast turnabout time is urgently needed for nephrectomy. Desorption Electrospray Ionization Mass Spectrometric Imaging (DESI-MSI) is an ideal technique for rapid identification and characterization of biological tissue specimens for real-time clinical applications since it is performed at room temperature and in open-air, can handle crude sample very easily with minimal sample preparation, and provides a comprehensive chemical map of metabolites. We and others have demonstrated that DESI-MSI distinguishes normal vs. malignant tissues based on the distribution of metabolite and lipid species obtained at tissue surfaces from several organs including pancreas, brain, breast, stomach, kidney and prostate. DESI-MSI classified PSM vs. negative surgical margin (NSM) with high sensitivity and specificity in several cohorts of prospectively collected surgical specimens, demonstrating the potential value of DESI-MSI in providing accurate and rapid assessment of surgical margin for intraoperative use. Our long-term goal is to develop and implement a rapid and reproducible method of assessing cancer margins during surgical resection of clear cell renal cell carcinoma (cccRCC) that will enable more complete cancer destruction. Our immediate objective is to evaluate the ability of DESI-MSI to rapidly assess cancer margins using fresh nephrectomy specimens and compare to IFS for sensitivity and specificity. To achieve this objective, we will first perform DESI-MSI on 30 paired frozen ccRCC and normal kidney tissues from our in-house biobank to identify a metabolite and lipid signature that distinguishes ccRCC from normal kidney tissue. We will then validate this signature in an independent validation set of 15 paired frozen ccRCC and normal kidney tissues. Finally, we will compare the accuracy of the DESI-MSI model to frozen section in determining tumor margins using 27 fresh nephrectomy specimens. If successful, we will generate a diagnostic metabolite and lipid profile that differentiates normal vs. cancer tissues in ccRCC. In addition, we will develop a novel sensitive method based on DESI-MSI to determine the presence of ccRCC cancer cells in fresh nephrectomy specimens that could guide surgeons in removing cancer tissue to achieve negative margins and improve surgical outcomes. !